Covid 19 Analysis: Delta Vatiant

Delta variant is dominant in many of the countries and threat to others

It is more easily spreadable variant and presumabily dangerous then other variants

We will analyse what are the factors for its spread and what can be done to control it

# Data pre-processing
import pandas as pd
import numpy as np 
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore") 
import seaborn as sns
import plotly as pl
import plotly.express as px
import plotly.graph_objects as go
%matplotlib inline

The following dataset contains bi weekly number of cases with respect to individual countries

Samples of possitive case are talken and checked for variants

The data are therefore subset of the whole positive cases

    #Reading a text file and storing it into a dataframe.
    CovidVariant = pd.read_csv('covid-variants1.csv')
    CovidVariant.head(9999999)

	location	date	variant	num_sequences	perc_sequences	num_sequences_total
0	Angola	2020-12-21	B.1.160	0.0	0.0	93
1	Angola	2020-12-21	B.1.620	0.0	0.0	93
2	Angola	2020-12-21	B.1.258	0.0	0.0	93
3	Angola	2020-12-21	B.1.221	NaN	NaN	93
4	Angola	2020-12-21	B.1.1.302	NaN	NaN	93
...	...	...	...	...	...	...
34564	Zimbabwe	2021-02-08	B.1.621	NaN	NaN	37
34565	Zimbabwe	2021-02-08	S:677P.Pelican	NaN	NaN	37
34566	Zimbabwe	2021-02-08	S:677H.Robin1	NaN	NaN	37
34567	Zimbabwe	2021-02-08	others	0.0	0.0	37
34568	Zimbabwe	2021-02-08	non_who	0.0	0.0	37

34569 rows × 6 columns

Task 1

How did the delta variant change the Covid-19 situation in countries like United Kingdom, India and Russia?

# Filtering out only Delta variant
Delta = CovidVariant.loc[CovidVariant['variant'] == 'Delta']

#Taking the maximim of vaccination counts
Delta1 = Delta.groupby(["location",'variant'])['num_sequences',
                     'num_sequences_total'].sum().reset_index()
Delta1[Delta1['location'] == 'India']

	location	variant	num_sequences	num_sequences_total
32	India	Delta	15568.0	39087

The following choropleth map represents the delta variant in each country

Here we can see that the UK has maximum number of delta variant. This is just due to UK has done more tests for variants.

 
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
trace = go.Choropleth(
            locations = Delta1['location'],
            locationmode='country names',
            z = Delta1['num_sequences'],
            text = Delta1['num_sequences_total'],
            autocolorscale =False,
            reversescale = True,
            colorscale = 'viridis',
            marker = dict(
                line = dict(
                    color = 'rgb(0,0,0)',
                    width = 0.5)
            ),
            colorbar = dict(
                title = 'Total Delta cases',
                tickprefix = '')
        )

data = [trace]
layout = go.Layout(
    title = 'Total Delta cases for country',
    geo = dict(
        showframe = True,
        showlakes = False,
        showcoastlines = True,
        projection = dict(
            type = 'natural earth'
        )
    )
)
fig = dict( data=data, layout=layout )
iplot(fig)

Same data as above just in a treemap chart to see from various prespective

fig = px.treemap(Delta1,values = 'num_sequences',
                 path = ['num_sequences_total','location'],
                 title="Total Delta variants for a country")
fig.show()

Selecting data only for India,UK, Russia and germany and some processing

India = CovidVariant.loc[CovidVariant['location'] == 'India']
UK = CovidVariant.loc[CovidVariant['location'] == 'United Kingdom']
RSA = CovidVariant.loc[CovidVariant['location'] == 'Russia']
GER = CovidVariant.loc[CovidVariant['location'] == 'Germany']
 

INDDelta = India.loc[India['variant'] == 'Delta']
UKDelta = UK.loc[UK['variant'] == 'Delta']
RSADelta = RSA.loc[RSA['variant'] == 'Delta']
GERDelta = GER.loc[GER['variant'] == 'Delta']

INDDelta['date'] = pd.to_datetime(INDDelta['date']) 
UKDelta['date'] = pd.to_datetime(UKDelta['date'])  
RSADelta['date'] = pd.to_datetime(RSADelta['date'])  
GERDelta['date'] = pd.to_datetime(GERDelta['date']) 

INDDelta.reset_index(inplace = True)
UKDelta.reset_index(inplace = True)
RSADelta.reset_index(inplace = True)
GERDelta.reset_index(inplace = True)

INDDelta = INDDelta[['location','date','variant','num_sequences','perc_sequences','num_sequences_total']]
UKDelta = UKDelta[['location','date','variant','num_sequences','perc_sequences','num_sequences_total']]
RSADelta = RSADelta[['location','date','variant','num_sequences','perc_sequences','num_sequences_total']]
GERDelta = GERDelta[['location','date','variant','num_sequences','perc_sequences','num_sequences_total']]

INDDelta = INDDelta.rename(columns={"num_sequences":"2_weeks_Delta_cases","perc_sequences":"2_weeks_Delta_cases_%","num_sequences_total":"All Variants"})
UKDelta = UKDelta.rename(columns={"num_sequences":"2_weeks_Delta_cases","perc_sequences":"2_weeks_Delta_cases_%","num_sequences_total":"All Variants"})
RSADelta = RSADelta.rename(columns={"num_sequences":"2_weeks_Delta_cases","perc_sequences":"2_weeks_Delta_cases_%","num_sequences_total":"All Variants"})
GERDelta = GERDelta.rename(columns={"num_sequences":"2_weeks_Delta_cases","perc_sequences":"2_weeks_Delta_cases_%","num_sequences_total":"All Variants"})

# Dividing by 14 to get average daily data from this bi weekly data format
INDDelta['Average Daily Delta variants'] = INDDelta['2_weeks_Delta_cases']/14
UKDelta['Average Daily Delta variants'] = UKDelta['2_weeks_Delta_cases']/14
RSADelta['Average Daily Delta variants'] = RSADelta['2_weeks_Delta_cases']/14
GERDelta['Average Daily Delta variants'] = GERDelta['2_weeks_Delta_cases']/14

#India Bi weekly delta cases
fig = go.Figure(data=[go.Scatter(
    x=INDDelta['date'], y=INDDelta['2_weeks_Delta_cases'],
    text=['A<br>size: 40', 'B<br>size: 60', 'C<br>size: 80', 'D<br>size: 100'],
    mode='markers',
    marker=dict(
        size=INDDelta['2_weeks_Delta_cases'],
        sizemode='area',
        sizeref=2.*max(INDDelta['2_weeks_Delta_cases'])/(40.**2),
        sizemin=4,
    )
)])

fig.show()

The following chart represents the total tested data for variants and delta variants in it for India

fig = go.Figure()
fig.add_trace(go.Bar(x=INDDelta['date'],
                y=INDDelta['All Variants'],
                name='All Variant Cases Found  ',
                marker_color='rgb(55, 83, 109)'
                ))
fig.add_trace(go.Bar(x=INDDelta['date'],
                y=INDDelta['2_weeks_Delta_cases'],
                name='Delta Variant cases reported',
                marker_color='rgb(26, 118, 255)'
                ))

fig.update_layout(
    title='India Delta Variant cases',
    xaxis_tickfont_size=14,
    yaxis=dict(
        title='Number of Total Cases vs Delta Variant Cases',
        titlefont_size=16,
        tickfont_size=14,
    ),
    legend=dict(
        x=0,
        y=1.0,
        bgcolor='rgba(255, 255, 255, 0)',
        bordercolor='rgba(255, 255, 255, 0)'
    ),
    barmode='group',
    bargap=0.15, # gap between bars of adjacent location coordinates.
    bargroupgap=0.1 # gap between bars of the same location coordinate.
)
fig.show()

#Scatter plot for India delta variant
from plotly.offline import iplot

daily_delta = go.Scatter(x=INDDelta['date'], y=INDDelta['Average Daily Delta variants'], yaxis='y2', name='Average daily Delta cases for 2 weeks')

layout_obj = go.Layout(title='Delta variant', xaxis=dict(title='Date'), yaxis=dict(title='Average daily Delta cases', side='right', overlaying='y'))
fig = go.Figure(data=[daily_delta], layout=layout_obj)
iplot(fig),

(None,)

#UK scatter plot of biweekly delta variant data
= go.Figure(data=[go.Scatter(
    x=UKDelta['date'], y=UKDelta['2_weeks_Delta_cases'],
    text=['A<br>size: 40', 'B<br>size: 60', 'C<br>size: 80', 'D<br>size: 100'],
    mode='markers',
    marker=dict(
        size=UKDelta['2_weeks_Delta_cases'],
        sizemode='area',
        sizeref=2.*max(UKDelta['2_weeks_Delta_cases'])/(40.**2),
        sizemin=4,
    )
)])

fig.show()

The following chart represents the total tested data for variants and delta variants in it for UK

fig = go.Figure()
fig.add_trace(go.Bar(x=UKDelta['date'],
                y=UKDelta['All Variants'],
                name='All  Variants cases reported',
                marker_color='rgb(55, 83, 109)'
                ))
fig.add_trace(go.Bar(x=UKDelta['date'],
                y=UKDelta['2_weeks_Delta_cases'],
                name='Delta Variant cases reported',
                marker_color='rgb(26, 118, 255)'
                ))

fig.update_layout(
    title='UK Delta Variant cases',
    xaxis_tickfont_size=14,
    yaxis=dict(
        title='Number of Total Cases vs Delta Variant Cases',
        titlefont_size=16,
        tickfont_size=14,
    ),
    legend=dict(
        x=0,
        y=1.0,
        bgcolor='rgba(255, 255, 255, 0)',
        bordercolor='rgba(255, 255, 255, 0)'
    ),
    barmode='group',
    bargap=0.15, # gap between bars of adjacent location coordinates.
    bargroupgap=0.1 # gap between bars of the same location coordinate.
)
fig.show()

#Scatter plot for Uk delta variant

from plotly.offline import iplot
daily_delta = go.Scatter(x=UKDelta['date'], y=UKDelta['Average Daily Delta variants'], yaxis='y2', name='Average daily Delta cases for 2 weeks')

layout_obj = go.Layout(title='UK Delta variant', xaxis=dict(title='Date'), yaxis=dict(title='Average daily cases'))
fig = go.Figure(data=[daily_delta], layout=layout_obj)
iplot(fig)

#Russia Scatter plot for bi weekly delta variant
fig = go.Figure(data=[go.Scatter(
    x=RSADelta['date'], y=RSADelta['2_weeks_Delta_cases'],
    text=['A<br>size: 40', 'B<br>size: 60', 'C<br>size: 80', 'D<br>size: 100'],
    mode='markers',
    marker=dict(
        size=RSADelta['2_weeks_Delta_cases'],
        sizemode='area',
        sizeref=2.*max(RSADelta['2_weeks_Delta_cases'])/(40.**2),
        sizemin=4,
    )
)])

fig.show()

The following chart represents the total tested data for variants and delta variants in it for Russia

fig = go.Figure()
fig.add_trace(go.Bar(x=RSADelta['date'],
                y=RSADelta['All Variants'],
                name='All  Variants cases reported',
                marker_color='rgb(55, 83, 109)'
                ))
fig.add_trace(go.Bar(x=RSADelta['date'],
                y=RSADelta['2_weeks_Delta_cases'],
                name='Delta Variant cases reported',
                marker_color='rgb(26, 118, 255)'
                ))

fig.update_layout(
    title='Russia Delta Variant cases',
    xaxis_tickfont_size=14,
    yaxis=dict(
        title='Number of Total Cases vs Delta Variant Cases',
        titlefont_size=16,
        tickfont_size=14,
    ),
    legend=dict(
        x=0,
        y=1.0,
        bgcolor='rgba(255, 255, 255, 0)',
        bordercolor='rgba(255, 255, 255, 0)'
    ),
    barmode='group',
    bargap=0.15, # gap between bars of adjacent location coordinates.
    bargroupgap=0.1 # gap between bars of the same location coordinate.
)
fig.show()

#Scatter plot Russia delta variant

from plotly.offline import iplot
daily_delta = go.Scatter(x=RSADelta['date'], y=RSADelta['Average Daily Delta variants'], yaxis='y2', name='Average daily Delta cases for 2 weeks')

layout_obj = go.Layout(title='Russia Covid vs Delta variant', xaxis=dict(title='Date'), yaxis=dict(title='Average daily cases'))
fig = go.Figure(data=[daily_delta], layout=layout_obj)
iplot(fig)

Comparision of delta cases between the UK, India and Russia

from plotly.offline import iplot

daily_deltaIND = go.Scatter(x=INDDelta['date'], y=INDDelta['2_weeks_Delta_cases'], yaxis='y2', name='Total Delta cases for 2 weeks India')


daily_deltaUK = go.Scatter(x=UKDelta['date'], y=UKDelta['2_weeks_Delta_cases'], yaxis='y2', name='Total Delta cases for 2 weeks UK')
daily_delta = go.Scatter(x=RSADelta['date'], y=RSADelta['2_weeks_Delta_cases'], yaxis='y2', name='Total Delta cases for 2 weeks RSA')

layout_obj = go.Layout(title='Delta variant', xaxis=dict(title='Date'), yaxis=dict(title='total cases'))
fig = go.Figure(data=[daily_deltaIND,daily_deltaUK,daily_delta], layout=layout_obj)
iplot(fig)                                                             

Delta2 = Delta1.loc[(Delta1['location']=='India') | (Delta1['location']=='United Kingdom')|(Delta1['location']=='Russia') ]

Delta2

	location	variant	num_sequences	num_sequences_total
32	India	Delta	15568.0	39087
64	Russia	Delta	1596.0	5934
81	United Kingdom	Delta	203735.0	607118

The following pie chart represents:

Right - Total varients tested in each countries

Left - Total number of delta variants found in each countries

import plotly.graph_objects as go
from plotly.subplots import make_subplots

labels = ["India","Russia",'United Kingdom']

# Create subplots: use 'domain' type for Pie subplot
fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])
fig.add_trace(go.Pie(labels=labels, values=Delta2['num_sequences'], name="Delta Variant cases"),
              1, 1)
fig.add_trace(go.Pie(labels=labels, values=Delta2['num_sequences_total'], name="Delta Variant tests"),
              1, 2)

fig.add_trace(go.Pie(labels=labels, values=Delta2['num_sequences_total'], name="Delta Variant tests"),
              1, 2)
# Use `hole` to create a donut-like pie chart
fig.update_traces(hole=.4, hoverinfo="label+value+name")

fig.update_layout(
    title_text="Delta Variant across 3 Countries",
    # Add annotations in the center of the donut pies.
    annotations=[dict(text=' 220899 Delta Cases', x=0.12, y=0.5, font_size=20, showarrow=False),
                 dict(text='652139 All Variant', x=0.87, y=0.5, font_size=20, showarrow=False)])
fig.show()

#Reading a text file and storing it into a dataframe.
Covid = pd.read_csv('countrySubmissionCount.csv')
Covid.head()

	Country	Total #Delta G/478K.V1 (B.1.617.2+AY.1+AY.2+AY.3)	#Delta G/478K.V1 (B.1.617.2+AY.1+AY.2+AY.3) in past 4 weeks	%Delta G/478K.V1 (B.1.617.2+AY.1+AY.2+AY.3) in past 4 weeks
0	United Kingdom	212,655	54,452	99.8
1	USA	54,615	19,444	92.4
2	Denmark	19,089	14,689	98.2
3	India	18,245	260	98.9
4	Germany	9,283	4,506	95.2

# Renaming Columns 
Covid = Covid.rename(columns={"Total #Delta G/478K.V1 (B.1.617.2+AY.1+AY.2+AY.3)":"Total Delta Cases","#Delta G/478K.V1 (B.1.617.2+AY.1+AY.2+AY.3) in past 4 weeks":"Delta Cases in past 4 weeks","%Delta G/478K.V1 (B.1.617.2+AY.1+AY.2+AY.3) in past 4 weeks":"Delta Cases in %"})

Covid

	Country	Total Delta Cases	Delta Cases in past 4 weeks	Delta Cases in %
0	United Kingdom	212,655	54,452	99.8
1	USA	54,615	19,444	92.4
2	Denmark	19,089	14,689	98.2
3	India	18,245	260	98.9
4	Germany	9,283	4,506	95.2
...	...	...	...	...
113	Dominican Republic	1	0	0.0
114	Tunisia	1	0	0.0
115	Azerbaijan	1	1	100.0
116	Colombia	1	0	0.0
117	Guam	1	0	0.0

118 rows × 4 columns

ccSc12= pd.read_csv('countrySubmissionCount.csv')
ccSc12.head()

	Country	Total #Delta G/478K.V1 (B.1.617.2+AY.1+AY.2+AY.3)	#Delta G/478K.V1 (B.1.617.2+AY.1+AY.2+AY.3) in past 4 weeks	%Delta G/478K.V1 (B.1.617.2+AY.1+AY.2+AY.3) in past 4 weeks
0	United Kingdom	212,655	54,452	99.8
1	USA	54,615	19,444	92.4
2	Denmark	19,089	14,689	98.2
3	India	18,245	260	98.9
4	Germany	9,283	4,506	95.2

In the following steps the percentage of delta cases are taken and applied it to total cases of a month period (7 July 2021 to 7 August 2021).

This gives the approximated total delta variant cases in each countries

ccSc12.rename(columns={"Total #Delta G/478K.V1 (B.1.617.2+AY.1+AY.2+AY.3)": "Total Cases", 
                       "#Delta G/478K.V1 (B.1.617.2+AY.1+AY.2+AY.3) in past 4 weeks": "Total Cases in 4 weeks",
                      "%Delta G/478K.V1 (B.1.617.2+AY.1+AY.2+AY.3) in past 4 weeks":"Percentage in 4 Weeks"}, inplace = True)
labels = ['United Kingdom', 'India', 'Russia']
threeCountries = ccSc12[ccSc12["Country"].isin(labels)]

# threeCountries.apply(threeCountries.to_numeric)
# threeCountries.iloc[:,:].str.replace(',', '').astype(float)
threeCountries['Total Cases'] = threeCountries['Total Cases'].str.replace(',','').astype('float')
threeCountries['Total Cases in 4 weeks'] = threeCountries['Total Cases in 4 weeks'].str.replace(',','').astype('float')

#Data Taken from Worldometer.com
# These data are from july 7 2021 to august 7 2021 for UK, India and Russia respectively
cases_in_4weeks = [1053882,1224983, 742250]
  
 
threeCountries['4 weeks total cases'] = cases_in_4weeks
threeCountries['4 weeks delta cases'] = (threeCountries['4 weeks total cases']/100) *threeCountries['Percentage in 4 Weeks']
threeCountries

	Country	Total Cases	Total Cases in 4 weeks	Percentage in 4 Weeks	4 weeks total cases	4 weeks delta cases
0	United Kingdom	212655.0	54452.0	99.8	1053882	1051774.236
3	India	18245.0	260.0	98.9	1224983	1211508.187
21	Russia	1591.0	63.0	98.4	742250	730374.000

The following bar graph represents total number of cases in 1 month span (7 July 2021 to 7 August 2021) and total number of delta variant

fig = go.Figure(data=[ 
    go.Bar(name='Total Cases in 4 weeks', x=labels, y=threeCountries['4 weeks total cases']),
   go.Bar(name='Percentage in 4 Weeks', x=labels, y=threeCountries['4 weeks delta cases'])
])  
fig.update_layout(
    title='4 week Cases Vs Predicted Delta variant Cases( 7 July 2021 - 7 August 2021)',
    xaxis_tickfont_size=14,
    yaxis=dict(
        title='Number of Total Cases ',
        titlefont_size=16,
        tickfont_size=14,
    )
)
fig.show() 

# This pie chart represents the number of tested, delta found and percentage of delta found in each countries 
from plotly.subplots import make_subplots
# Create subplots: use 'domain' type for Pie subplot
labels = ['United Kingdom', 'India', 'Russia']
fig = make_subplots(rows=1, cols=3, specs=[[ {'type':'domain'}, {'type':'domain'},{'type':'domain'}]])
fig.add_trace(go.Pie(labels=labels, values=threeCountries['Total Cases'], name="Delta Variant cases"),
              1, 1)
fig.add_trace(go.Pie(labels=labels, values=threeCountries['Total Cases in 4 weeks'], name="Cases in 4 weeks"),
              1, 2)
fig.add_trace(go.Pie(labels=labels, values=threeCountries['Percentage in 4 Weeks'], name="Percentage in 4 weeks"),
              1, 3)

# Use `hole` to create a donut-like pie chart
fig.update_traces(hole=.4, hoverinfo="label+value+name")

fig.update_layout(
    title_text="Delta Variant across 3 Countries",
    # Add annotations in the center of the donut pies.
 annotations=[dict(text=threeCountries['Total Cases'].sum(), x=0.08, y=0.5, font_size=20, showarrow=False),
                 dict(text=threeCountries['Total Cases in 4 weeks'].sum(), x=0.50, y=0.5, font_size=20, showarrow=False),
             dict(text="Total Case", x=0.92, y=0.5, font_size=20, showarrow=False)])
fig.show()

import plotly.express as px
labels = ['United Kingdom', 'India', 'Russia']
India = CovidVariant.loc[CovidVariant['location'] == 'India']
UK = CovidVariant.loc[CovidVariant['location'] == 'United Kingdom']
RSA = CovidVariant.loc[CovidVariant['location'] == 'Russia']
GER = CovidVariant.loc[CovidVariant['location'] == 'Germany']
df = CovidVariant[CovidVariant['location'].isin(labels)]
df=df[df["variant"]=="Delta"]
df1 = pd.DataFrame(df, columns=["location","date","num_sequences"])
df4 = px.data.stocks()
df1 

	location	date	num_sequences
12938	India	2020-05-11	0.0
12961	India	2020-05-25	0.0
12984	India	2020-06-08	0.0
13007	India	2020-06-22	0.0
13030	India	2020-07-06	0.0
...	...	...	...
33224	United Kingdom	2021-06-14	33616.0
33247	United Kingdom	2021-06-28	42295.0
33270	United Kingdom	2021-07-12	53571.0
33293	United Kingdom	2021-07-26	43466.0
33316	United Kingdom	2021-08-07	6968.0

99 rows × 3 columns

The following plot is a logarithmic transformation of data to see the pattern/ trend more easily

It represents when delta variant is emersed and how it is spreading

from plotly.offline import iplot


daily_deltaIND = go.Scatter(x=INDDelta['date'], y=np.log10(INDDelta['2_weeks_Delta_cases']), name='India')


daily_deltaUK = go.Scatter(x=UKDelta['date'], y=np.log10(UKDelta['2_weeks_Delta_cases']), name='UK')
daily_delta = go.Scatter(x=RSADelta['date'], y=np.log10(RSADelta['2_weeks_Delta_cases']),  name='RSA')

layout_obj = go.Layout(title='Delta variant', xaxis=dict(title='Date'), yaxis=dict(title='total cases'))
fig = go.Figure(data=[daily_deltaIND,daily_deltaUK,daily_delta], layout=layout_obj)
iplot(fig)

The following chart represents the Covid 19 cases from the begining and Delta case together

from plotly.offline import iplot

daily_deltaI = go.Scatter(x=INDDelta['date'], y=INDDelta['All Variants'], name='Total India')
daily_deltaU = go.Scatter(x=UKDelta['date'], y=UKDelta['All Variants'], name='Total UK')
daily_deltaR = go.Scatter(x=RSADelta['date'], y=RSADelta['All Variants'],  name='Total RSA')

daily_deltaIND = go.Scatter(x=INDDelta['date'], y=INDDelta['2_weeks_Delta_cases'], name='Delta India')
daily_deltaUK = go.Scatter(x=UKDelta['date'], y=UKDelta['2_weeks_Delta_cases'], name='Delta UK')
daily_delta = go.Scatter(x=RSADelta['date'], y=RSADelta['2_weeks_Delta_cases'],  name='Delta RSA')

layout_obj = go.Layout(title='Delta variant Vs All Variants', xaxis=dict(title='Date'), yaxis=dict(title='total cases'))
fig = go.Figure(data=[daily_deltaI,daily_deltaU,daily_deltaR, daily_deltaIND,daily_deltaUK,daily_delta], layout=layout_obj)
iplot(fig)

The logarithmic chart of above figure where we can see how Delta variant has started and be dominant among all other variants

daily_deltaI = go.Scatter(x=INDDelta['date'], y=np.log10(INDDelta['All Variants']),  name='Total India')
daily_deltaU = go.Scatter(x=UKDelta['date'], y=np.log10(UKDelta['All Variants']), name='Total UK')
daily_deltaR = go.Scatter(x=RSADelta['date'], y=np.log10(RSADelta['All Variants']),  name='Total RSA')

daily_deltaIND = go.Scatter(x=INDDelta['date'], y=np.log10(INDDelta['2_weeks_Delta_cases']), name='Delta India')
daily_deltaUK = go.Scatter(x=UKDelta['date'], y=np.log10(UKDelta['2_weeks_Delta_cases']), name='Delta UK')
daily_delta = go.Scatter(x=RSADelta['date'], y=np.log10(RSADelta['2_weeks_Delta_cases']),  name='Delta RSA')

layout_obj = go.Layout(title='Delta variant Vs All Variants', xaxis=dict(title='Date'), yaxis=dict(title='total cases'))
fig = go.Figure(data=[daily_deltaI,daily_deltaU,daily_deltaR, daily_deltaIND,daily_deltaUK,daily_delta], layout=layout_obj)
iplot(fig)

country= pd.read_csv('countries-aggregated.csv')
germany=country[country['Country']=='Germany']
india=country[country['Country']=='India']

Task 4 Part 1

The following chart represents the cases and deaths due to Covid 19 from begining till 10 August 2021

Also the first lockdown peiod is annonated to show that lockdown has helped to stop the drastic growth of positive cases and death

from plotly.subplots import make_subplots
 
fig = make_subplots(specs=[[{"secondary_y": True}]])

fig.add_trace(
      go.Scatter(x=germany['Date'], y= germany['Confirmed'], name='Germany Confirmed ') ,
    secondary_y=False,
)
fig.add_trace(   
      go.Scatter(x=india['Date'], y= india['Confirmed'], name=' India Confirmed ') ,
    secondary_y=False,
)

fig.add_trace(
      go.Scatter(x=germany['Date'], y= germany['Deaths'], name=' Germany Deaths '),
    secondary_y=True,
) 
fig.add_trace(
      go.Scatter(x=india['Date'], y= india['Deaths'], name='India Deaths '),
    secondary_y=True,
) 
fig.update_yaxes(title_text="Number of Positive Cases", secondary_y=False)
fig.update_yaxes(title_text="Number of Total Deaths", secondary_y=True)

  
fig.add_annotation(x=60, secondary_y=5000,
            text="Starts",
            showarrow=True,
            arrowcolor="#636363",
            arrowsize=2,
            arrowwidth=1,
            arrowhead=2) 

fig.add_annotation(x=110, y=0.3,
            text=" Eased  ",
            showarrow=True,
            arrowcolor="#636363",
            arrowsize=2,
            arrowwidth=1,
            arrowhead=2) 
  

iplot(fig)

Thank you